Artificial Intelligence (AI)

9 producten


  • Google Coral USB Accelerator

    Google Google Coral USB Accelerator

    De Coral USB Accelerator voegt een Edge TPU coprocessor toe aan uw systeem. Het simpelweg aansluiten van deze accelerator op een USB poort maakt high-speed machine learning inferencing op een breed scala aan systemen mogelijk. Kenmerken Ondersteunde host OS: Debian Linux, macOS, Windows 10 Compatible met Raspberry Pi boards Ondersteund Framework: TensorFlow Lite Voert high-speed ML inferencing uit De on-board Edge TPU-coprocessor is in staat om 4 biljoen bewerkingen (tera-operations) per seconde (TOPS) uit te voeren, met behulp van 0,5 watt voor elke TOPS (2 TOPS per watt). Hij kan bijvoorbeeld op een energiezuinige manier state-of-the-art mobile vision modellen, zoals MobileNet v2, uitvoeren met bijna 400 FPS. Ondersteunt alle belangrijke platforms Werkt via een USB poort met elk systeem met Debian Linux (inclusief Raspberry Pi), macOS of Windows 10. Ondersteunt TensorFlow Lite Het is niet nodig om modellen van de grond af aan op te bouwen. TensorFlow Lite modellen kunnen worden gecompileerd om op de Edge TPU te worden uitgevoerd. Ondersteunt AutoML Vision Edge Bouw en implementeer, eenvoudig en op maat, snelle en uiterst nauwkeurige image classification modellen op uw apparaat met AutoML Vision Edge. Specificaties ML accelerator Google Edge TPU coprocessor:4 TOPS (int8); 2 TOPS per watt Connector USB 3.0 Type-C (data/power) Dimensies 65 x 30 mm Downloads/Documentatie Datasheet Get started with the USB Accelerator Model compatibility on the Edge TPU Edge TPU inferencing overview Run multiple models with multiple Edge TPUs Pipeline a model with multiple Edge TPUs PyCoral API (Python) Libcoral API (C++) Libedgetpu API (C++) Edge TPU compiler Pre-compiled models All software downloads

    € 89,95

    Leden € 80,96

  • ESP32-S3-BOX-3

    Espressif ESP32-S3-BOX-3

    Niet op voorraad

    De ESP32-S3-BOX-3 is gebaseerd op Espressif's ESP32-S3 Wi-Fi + Bluetooth 5 (LE) SoC, met AI versnellingsmogelijkheden. Als toevoeging op de 512 KB SRAM van de ESP32-S3 wordt de ESP32-S3-BOX-3 geleverd met 16 MB Quad flash en 16 MB Octaal PSRAM. De ESP32-S3-BOX-3 draait op Espressif's eigen spraakherkenningsframework, ESP-SR, dat gebruikers een offline AI spraakassistent biedt. Hij beschikt over far-field voice interaction, continue herkenning, wake-up interruption, en de mogelijkheid om meer dan 200 aanpasbare opdrachtwoorden te herkennen. De BOX-3 kan ook worden omgevormd tot een online AI chatbot met behulp van geavanceerde AIGC ontwikkelingsplatforms, zoals OpenAI. Aangestuurd door de krachtige ESP32-S3 SoC biedt de BOX-3 ontwikkelaars een kant-en-klare oplossing voor het maken van Edge AI en HMI toepassingen. De geavanceerde functies en mogelijkheden van de BOX-3 maken hem tot een ideale keuze voor diegenen in de IIoT industrie die Industrie 4.0 willen omarmen en traditionele fabrieksbesturingssystemen willen transformeren. De ESP32-S3-BOX-3 is de hoofd unit die wordt aangestuurd door de ESP32-S3-WROOM-1 module, die 2,4 GHz Wi-Fi + Bluetooth 5 (LE) biedt, evenals AI versnellingsmogelijkheden. In aanvulling op de 512 KB SRAM van de ESP32-S3 SoC wordt de module geleverd met een extra 16 MB Quad flash en 16 MB Octal PSRAM. Het board is uitgerust met een 2,4-inch 320 x 240 SPI-touchscreen (de 'rode cirkel' ondersteunt aanraking), twee digitale microfoons, een luidspreker, een 3-assige gyroscoop, een 3-assige accelerometer, een Type-C poort voor voeding en downloaden/debuggen, een high-density PCIe-connector die uitbreiding van de hardware mogelijk maakt, en ook drie functionele knoppen. Kenmerken ESP32-S3 WiFi + Bluetooth 5 (LE) 512 KB SRAM ESP32-S3-WROOM-1 16 MB Quad flash 16 MB Octaal PSRAM Inbegrepen ESP32-S3-BOX-3 Eenheid ESP32-S3-BOX-3 Sensor ESP32-S3-BOX-3 Dock ESP32-S3-BOX-3 Beugel ESP32-S3-BOX-3 Bread RGB LED-module en Dupont draden USB-C kabel Downloads GitHub Video's Unboxing The Next-generation Open-source Kit

    Niet op voorraad

    € 84,95

    Leden € 76,46

  • NVIDIA Jetson Nano Developer Kit

    Nvidia NVIDIA Jetson Nano Developer Kit (B01)

    Ready to start developing Artificial Intelligence (AI) applications? The NVIDIA Jetson Nano Developer Kit makes the power of modern AI accessible to makers, developers, and students. When you think of NVIDIA, you probably think about graphics cards and GPUs, and rightfully so. Nvidia's track record guarantees that the Jetson Nano has enough power to run even the most demanding of tasks. The NVIDIA Jetson Nano Developer Kit is compatible with Nvidia's JetPack SDK and enables image classification and object detection amongst many applications. Toepassingen The NVIDIA Jetson Nano Developer Kit can run multiple neural networks in parallel for applications like: Image classification Segmentation Object detection Speech processing Specificaties GPU 128-core Maxwell CPU Quad-core ARM A57 @ 1.43 GHz Memory 4 GB 64-bit LPDDR4 25.6 GB/s Storage microSD (not included) Video Encode 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265) Video Decode 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265) Camera 1 x MIPI CSI-2 DPHY lanes Connectivity Gigabit Ethernet, M.2 Key E Display HDMI 2.0 and eDP 1.4 USB  4x USB 3.0, USB 2.0 Micro-B Interfaces GPIO, I²C, I²S, SPI, UART Dimensions 100 x 80 x 29 mm Inbegrepen NVIDIA Jetson Nano module and carrier board Small paper card with quick start and support information Folded paper stand Downloads JetPack SDK Documentation. Tutorials Online course Wiki

    € 229,00

    Leden € 206,10

  • Bijna uitverkocht HuskyLens AI Camera met Silicone Case

    HuskyLens AI Camera

    Heeft u een eenvoudige AI-camera nodig om uw projecten te verbeteren?  Het intuïtieve ontwerp van de HuskyLens AI-camera stelt de gebruiker in staat om verschillende aspecten van de camera te bedienen door gewoon op knoppen te drukken. U kunt het leren van nieuwe objecten starten en stoppen en zelfs schakelen tussen algoritmes vanaf het apparaat. Om de noodzaak van aansluiting op een pc verder te verminderen, wordt de HuskyLens AI Camera geleverd met een 2-inch display, zodat u in realtime kunt zien wat er gaande is. Specificaties Processor: Kendryte K210 Beeldsensor: OV2640 (2.0 Megapixel Camera) Voedingsspanning: 3,3~5,0 V Stroomverbruik (TYP): 320 mA bij 3,3 V, 230 mA bij 5,0 V (gezichtsherkenningsmodus; 80% helderheid achtergrondverlichting; vullicht uit) Aansluitingsinterface: UART, I²C Display: 2,0-inch IPS-scherm met 320x240 resolutie Ingebouwde algoritmen: Gezichtsherkenning, Object Tracking, Object Recognition, Line Tracking, Kleurherkenning, Tag Recognition Afmeting: 52 x 44,5 mm Inbegrepen 1x HuskyLens moederbord 1x M3 Schroeven 1x M3 Moeren 1x Kleine montagebeugel 1x Verhoogingsbeugel 1x Zwaartekracht 4-Pin Sensor Kabel

    € 89,95

    Leden € 80,96

  •  -20% M5Stack UnitV K210 AI Camera for Edge Computing (OV7740)

    M5Stack M5Stack UnitV K210 AI Camera for Edge Computing (OV7740)

    Features Dual-Core 64-bit RISC-V RV64IMAFDC (RV64GC) CPU / 400Mhz(Normal) Dual Independent Double Precision FPU 8MiB 64bit width On-Chip SRAM Neural Network Processor(KPU) / 0.8Tops Field-Programmable IO Array (FPIOA) AES, SHA256 Accelerator Direct Memory Access Controller (DMAC) Micropython Support Firmware encryption support On-board Hardware: Flash: 16M Camera :OV7740 2x Buttons Status Indicator LED External storage: TF card/Micro SD Interface: HY2.0/compatible GROVE Applications Face recognition/detection Object detection/classification Obtain the size and coordinates of the target in real-time Obtain the type of detected target in real-time Shape recognition Video recorder Included 1x UNIT-V(include 20cm 4P cable and USB-C cable)

    € 34,95€ 27,95

    Leden identiek

  •  -21%Bijna uitverkocht M5Stack UnitV2 AI Camera for Edge Computing

    M5Stack M5Stack UnitV2 AI Camera for Edge Computing

    Nog 1 op voorraad

    Features Sigmstar SSD202D Dual Cortex-A7 1.2Ghz Processor 128MB DDR3 512MB NAND Flash GC2145 1080P Colored Sensor Microphone WiFi 2.4GHz Applications AI recognition function development Industrial visual identification sorting Machine vision learning Included 1 x M5Stack UnitV2 1 x 16g TF Card 1 x USB-C Cabel (50cm) 1 x Stand 1 x Back Brick

    Nog 1 op voorraad

    € 94,95€ 74,95

    Leden identiek

  • Google AIY Vision Kit for Raspberry Pi

    Google Google AIY Vision Kit for Raspberry Pi

    Niet op voorraad

    Google AIY Projects brings do-it-yourself artificial intelligence to your maker projects. The Google AIY Vision Kit lets you build an image recognition device that can see and identify objects, powered by TensorFlow’s machine learning models.The kit includes all of the components needed to assemble the basic kit that works with the Google Assistant SDK as well as on-device image & vision recognition with TensorFlow using the Intel Movidius Myriad Vision Processing Unit (VPU) hardware assist.Assembling the kit should take about one hour. There is no-soldering-required, complete AIY kit is an awesome Pi Zero-powered project!Included Vision Bonnet Board with Movidius VPU for Raspberry Pi - fully assembled Raspberry Pi Zero WH (Fully assembled) Raspberry Pi Camera Board Pi Zero Camera flat flex cable CSI flat flex cable to connect to the camera MicroSD card for the operating system 11mm Plastic standoffs Privacy LED Arcade Push Button Button harness Piezo buzzer LED bezel USB cable - A/MicroB Tripod mounting nut External cardboard box Internal cardboard frame

    Niet op voorraad

    € 104,95

    Leden € 94,46

  • Seeed Studio XIAO ESP32S3 Sense

    Seeed Studio Seeed Studio XIAO ESP32S3 Sense

    Seeed Studio XIAO ESP32S3 Sense integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board can be your great tool to get started with intelligent voice and vision AI. Seeed Studio XIAO ESP32S3 Sense is built around a highly-integrated, Xtensa processor ESP32-S3R8 SoC, which supports 2.4 GHz WiFi and low-power Bluetooth BLE 5.0 dual-mode for multiple wireless applications. It has lithium battery charge management capability. As the advanced version of Seeed Studio XIAO ESP32S3, this board comes with a plug-in OV2640 camera sensor for displaying full 1600x1200 resolution. The base of it is even compatible with OV5640 for supporting up to 2592x1944 resolution. The digital microphone is also carried with the board for voice sensing and audio recognition. SenseCraft AI provides various pre-trained Artificial Intelligence (AI) models and no-code deployment to XIAO ESP32S3 Sense. With powerful SoC and built-in sensors, this development board has 8 MB PSRAM and 8 MB Flash on the chip, an additional SD card slot for supporting up to 32 GB FAT memory. These allow the board for more programming space and bring even more possibilities into embedded ML scenarios. Features Powerful MCU Board: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino/MicroPython supported Advanced Functionality: with OV5640 camera sensor, integrating additional digital microphone Great Memory for more Possibilities: Offer 8 MB PSRAM and 8 MB Flash, supporting SD card slot for external 32 GB FAT memory Outstanding RF performance: Support 2.4 GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna Thumb-sized Compact Design: 21 x 17.5 mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices Pretrained Al model from SenseCraft Al for no-code deployment Applications Image processing Speech Recognition Video Monitoring Wearable devices Smart Homes Health monitoring Education Low-Power (LP) networking Rapid prototyping Specifications Processor ESP32-S3R8 Xtensa LX7 dual-core, 32-bit processor that operates at up to 240 MHz Wireless Complete 2.4 GHz Wi-Fi subsystem BLE: Bluetooth 5.0, Bluetooth mesh Built-in Sensors oV2640 camera sensor for 1600x1200 Digital Microphone Memory On-chip 8 MB PSRAM & 8 MB Flash Onboard SD Card Slot, supporting 32 GB FAT lnterface 1x UART, 1x I²C, 1x I²S, 1x SPI, 11x GPIOs (PWM), 9x ADC, 1x User LED, 1x Charge LED, 1x B2B Connector (with 2 additional GPIOs) 1x Reset button, 1x Boot button Dimensions 21 x 17.5 x 15 mm (with expansion board) Power Input voltage (Type-C): 5 V lnput voltage (BAT): 4.2 V Circuit operating Voltage (ready to operate): - Type-C: 5 V @ 38.3 mA - BAT: 3.8 V @ 43.2 mA (with expansion board) Webcam Web application: Type-C: - Average power consumption: 5 V/138 mA - Photo moment: 5 V/341 mA Battery: - Average power consumption: 3.8 V/154 mA - Photo moment: 3.8 V/304 mA Microphone recording & SD card writing: Type-C: - Average power consumption: 5 V/46.5 mA - Peak power consumption: 5 V/89.6 mA Battery: - Average power consumption: 3.8 V/54.4 mA - Peak power consumption: 3.8 V/108 mA Charging battery current: 100 mA Low Power Consumption Model (Supply Power: 3.8 V) Modem Sleep Model: ~44 mA Light Sleep Model: ~5 mA Deep Sleep Model: ~3 mA Wi-Fi Enabled Power Consumption Active Model: ~ 110 mA (with expansion board) BLE Enabled Power Consumption Active Model: ~ 102 mA (with expansion board) Included 1x XIAO ESP32S3 1x Plug-in camera sensor board 1x Antenna Downloads GitHub

    € 24,95

    Leden € 22,46

  • AI at the Edge

    O'Reilly Media AI at the Edge

    Solving Real-World Problems with Embedded Machine LearningEdge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target—from ultra-low power microcontrollers to embedded Linux devices.This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. Develop your expertise in AI and ML for edge devices Understand which projects are best solved with edge AI Explore key design patterns for edge AI apps Learn an iterative workflow for developing AI systems Build a team with the skills to solve real-world problems Follow a responsible AI process to create effective products Downloads Errata GitHub

    € 79,95

    Leden € 71,96

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